extfieldsofstudy:
Computer Science
Medicine

text:
Drug–target interaction prediction via multiple classification strategies

Background Computational prediction of the interaction between drugs and protein targets is very important for the new drug discovery, as the experimental determination of drug-target interaction (DTI) is expensive and time-consuming. However, different protein targets are with very different numbers of interactions. Specifically, most interactions focus on only a few targets. As a result, targets with larger numbers of interactions could own enough positive samples for predicting their interactions but the positive samples for targets with smaller numbers of interactions could be not enough. Only using a classification strategy may not be able to deal with the above two cases at the same time. To overcome the above problem, in this paper, a drug-target interaction prediction method based on multiple classification strategies (MCSDTI) is proposed. In MCSDTI, targets are firstly divided into two parts according to the number of interactions of the targets, where one part contains targets with smaller numbers of interactions (TWSNI) and another part contains targets with larger numbers of interactions (TWLNI). And then different classification strategies are respectively designed for TWSNI and TWLNI to predict the interaction. Furthermore, TWSNI and TWLNI are evaluated independently, which can overcome the problem that result could be mainly determined by targets with large numbers of interactions when all targets are evaluated together. Results We propose a new drug-target interaction (MCSDTI) prediction method, which uses multiple classification strategies. MCSDTI is tested on five DTI datasets, such as nuclear receptors (NR), ion channels (IC), G protein coupled receptors (GPCR), enzymes (E), and drug bank (DB). Experiments show that the AUCs of our method are respectively 3.31%, 1.27%, 2.02%, 2.02% and 1.04% higher than that of the second best methods on NR, IC, GPCR and E for TWLNI; And AUCs of our method are respectively 1.00%, 3.20% and 2.70% higher than the second best methods on NR, IC, and E for TWSNI. Conclusion MCSDTI is a competitive method compared to the previous methods for all target parts on most datasets, which administrates that different classification strategies for different target parts is an effective way to improve the effectiveness of DTI prediction.

Background
Drug development is a time-consuming and expensive process that is plagued with the problem known as the high attrition rate. This led to the practitioners' great interest in drug repositioning due to its potential to reduce the time, cost, risk and effort inherent in developing new drugs. Some drug-target interaction (DTI) prediction methods have been proposed in the past several years, which can be divided into two categorists: similarity based methods and feature based methods.
Similarity based methods mainly use the similarity relationships between samples. Some similarity based methods proposed new optimization objective functions for similarity decomposition [1][2][3][4][5]. Ban et al. proposed a neighborhood regularized logistic matrix factorization [1], which can utilize the neighborhood information. Cui et al. proposed a L2,1 graph regularized matrix factorization to learn flow patterns in combination with the previous matrix-decomposition method [2]. Li et al. proposed a multi-view low rank embedding to integrate multi-view representations of drugs and proteins [3]. Mongia et al. proposed a multi-graph regularized nuclear norm minimization based method for DTI, which predicts the interactions between drugs and target proteins from three inputs [4]. Wang et al. proposed an effective computational model of dual Laplacian graph regularized matrix completion, where the drug and the target similarities can be fully exploited by using a dual Laplacian graph regularization term [5].
Although designing different optimization objective functions can make the decomposition factor meet different conditions, the decomposition factor heavily depended on similarity. Some similarity based methods designed a new method to calculate the similarity [6][7][8]. Zong et al. calculate the similarities within linked tripartite network, which enhanced existing association discovery methods by using a topology-based similarity measure [6]. Ding et al. developed a fuzzy bipartite local model, where multiple kernels are constructed in drug and target spaces [7]. Fan et al. introduced the similarity information of drugs/targets, and proposed the neighborhood constraint to regularize the unknown cases [8]. However, because the distributions of drugs and targets are very complex, it is hardly to design a good similarity calculation method. To overcome this problem and in order to make better use of the information contained in the feature, some feature-based methods have also been proposed.
Firstly, the feature is very important for the feature based methods, and some researcher proposed new feature extraction methods to extract more features from targets and drugs [10][11][12][13][14][15]. Li et al. used rotation forest in DTI, where local phase quantization descriptors are used to extract evolutionary information in the positionspecific scoring matrix (PSSM) [10]. Farshid et al. used Adaboost in DTI, where many feature extraction methods were used in the same time [11]. Jiang et al. proposed an ensemble system integrating k nearest neighbor classifier with a novel feature encoding scheme to identify DTI [12]. Mahmud et.al predicted DTI based on drug chemical structure and protein sequence by using extreme gradient boosting (XGBoost) with synthetic minority oversampling technique (SMOTE) [13]. Han et al. predicted DTI by using Lasso with random forest based on evolutionary information and chemical structure [14]. Xu et al. infer the DTI by using graph isomorphic network and word vector matrix [15].
Secondly, because it is unclear which feature is the best, many features could be extracted for the target and the drug in the same time [16][17][18], and then some dimensional reduction methods have been proposed for DTI [19][20][21][22][23]. Ezzat et al. proposed a framework for DTI prediction by leveraging both feature dimensionality reduction and ensemble learning [19]. Aman et al. proposed a bagging based ensemble framework named for DTI prediction by using dimensionality reduction and active learning to deal with class-imbalanced data [20]. Mahmud et al. predicted DTI based on protein features with under sampling and feature selection techniques with boosting [21]. Feng et al. proposed a supervised discriminative sparse principal component analysis [22] and a graph Laplacian sparse principal component analysis for dimensional reduction [23] Thirdly, some new classifiers are also proposed for DTI [24][25][26][27][28][29][30]. He et al. presented a method called SimBoost that predicts continuous values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions [24]. Rayhan et al. proposed an ensemble model which uses extra tree as weak learners inside a boosting scheme while holding on to the best model per iteration [25]. Pliakos et al. proposed a new learning method which addresses DTI prediction as a multi-output prediction task by learning ensembles of multi-output bi-clustering trees on reconstructed networks [26]. Zhang et al. used several random projections to build an ensemble random projection tree system [27]. Buza et al. selected a random subset of features and used only the selected features when training the local models [28]. Ezzat et al. proposed another ensemble learning method that incorporates techniques to address the issues of between class imbalance and within-class imbalance [29]. Ye et al. proposed a multiple output deep neural network to enhance the deep neural network learning ability with a kind of auxiliary classifier layers [30].
Although the above methods can solve some problems from different sides, they do not solve the problem that different targets are with very different numbers of interactions. For targets with larger numbers of interactions (TWLNI), many positive samples can be generated. But for targets with smaller numbers of interactions (TWSNI), so few interactions can only produce a small number of positive samples. As a result, different classification strategies should be designed for these two types of targets. Based on the above idea, in this paper, a new DTI prediction method based on multiple classification strategies (MCSDTI) is proposed.
In MCSDTI, targets are firstly divided into TWLNI and TWSNI. For TWLNI, because drug-target interactions are very sparsely distributed in the drug-target pair space, predicting interactions for these targets together with their neighbors could introduce more negative samples than positive samples. Furthermore, these targets could own enough positive samples for predicting their interactions. So interactions of TWLNI are predicted by using their owned positive samples. For TWSNI, numbers of positive samples of targets are too small. So the positive samples of their neighbors are used together to predict their interactions. As a result, using different classification strategies in different situations can make better use of the advantages of these classification strategies. What's more, TWLNI and TWSNI are evaluated independently, as the result could be mainly determined by TWLNI when TWLNI and TWSNI are evaluated together.
The contribution of this paper can be concluded as follows: (1) As far as we known, this is the first time that interactions of TWLNI and TWSNI are predicted by different classification strategies, which can make better use of the advantages of these classification strategies in different situations. (2) TWLNI and TWSNI are evaluated independently, which can overcome the problem that the improvement for TWSNI could be overwhelmed when TWLNI and TWSNI are evaluated together. (3) Designe a new classifier and a new evaluator for TWLNI, which can overcome the negative impact of samples of the neighbors. (4) Find a good classifier for TWSNI, whose effect for TWSNI has been overwhelmed by TWLNI. (5) Provide a new research idea for DTI prediction, as interactions of TWLNI and TWSNI cannot be predicted in the same time.
The remaining of this paper is organized as follows. Section 2 introduces the Methods. Section 3 introduces the results. Finally, Section 4 gives concluding remarks.

Data and motivation
Five datasets are used in this work, such as nuclear receptors (NR) [31], ion channels(IC) [31], G protein coupled receptors (GPCR) [31] and enzymes (E) [31], and drug bank (DB) [32]. The simplified molecular input line entry system (SIMILES) of drugs and sequences of targets are offered by these datasets, which can be used to extract the features for drugs and targets. The simple statistics for five datasets are given in the Table 1, where the 2nd to 4th rows respectively represented the number of drugs, targets and interactions, the 5th row represented the proportion of interactions among drug-target pair space.
By analyzing these datasets, two conclusions can be obtained. Firstly, drug-target interactions are very sparsely distributed in the drug-target pair space, which can be shown by the Table 1. It can be seen from the 5-th row of the Table 1 that the percentage of interactions in the drug-target pairs space are only 6.4%, 3.0%, 3.4% 0.99%, 0.064% respectively on NR, GPCR, IC, E and DB, which shows that the number of interactions is much smaller than the number of drug-target pairs. Secondly, most of the interactions focus on only a few targets, which can be shown by the Fig. 1. Distributions of interactions on four datasets are given. Targets are divided into five parts according to the numbers of interactions of targets and each part owns the same number of targets, where targets in the 1-th part owns smaller numbers of interactions, targets in the 2nd part owns larger numbers of interactions, targets in the 3rd part owns more large numbers of interactions and so on. It can be seen from Fig. 1 that more than 60% of interactions focus on 20% of targets on GPCR, E, DB, and nearly 50% of interactions focus on 20% of targets on NR and IC. And then some targets are with larger numbers of interactions, but other targets are with smaller numbers of interactions.
As a result, it is difficult to design a prediction strategy that can handle all these cases. So in this paper, different classification strategies are designed for these two types of targets.
To predict the DTI for a drug target pair, the feature vectors for the drug and the target should be firstly extracted. Some types of features have been proposed for the drugs, such as molecular substructure fingerprints, constitutional, topological, quantum chemical properties, and geometrical. Here the PubChem molecular substructure fingerprint is extracted for the drug by PaDEL [33], where the input of PaDEL is the SMILES of the drug. The extracted drug feature is defined as D. In this type of representation, each molecular structure is described by a Boolean vector, which is a   [34]. This feature gives a direct relationship between the molecular and properties and retain the entire structure of the drug molecule [34].
More types of features have been also proposed for targets, such as amino acid composition, dipeptide composition, autocorrelation descriptors, composition, transition, distribution, quasi-sequence-order descriptors, pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, topological descriptors for atom model, total amino acid properties. In this paper, all above features are extracted for targets by Protein features (PROFEAT) [35], where the input of PROFEAT is the sequence of the target. These features can describe the target from different aspects and the dimension of these features is not very big. The extracted target feature is defined as T.
The simple information of the extracted features is represented in the Table 2. It can be seen from Table 2 that dimensions of the drug feature, target feature and total feature are respectively 1024, 1437 and 2461. Furthermore, it also can be seen from the 4-th row of Table 1 that number of interactions of NR, GPCR, IC, E and DB are respectively 90, 635, 1476, 2926 and 12,674. Obviously, this is a high-dimensional small sample problem, which will be considered in designing classification strategies.

Overview of MCSDTI
Given drug features D, target features T, interaction matrix Y, drug similar S d and target similar S t , the flowchart of MCSDTI is shown. It can be seen from Fig. 2 that MCSDTI has 5 steps, where 1st and 5th steps are the input step and the output step.
Step 2 to step 4 will be simply introduced in the following.  In the preprocessing step, the targets are divided into TWLNI and TWSNI according to the number of interactions, where TWLNI contains targets with larger numbers of interactions, and TWSNI contains targets with smaller numbers of interactions. In the classification step, the TWLNI classifier and the TWSNI classifier are respectively designed for TWLNI and TWSNI, which can make better use of the advantages of these classifiers in different situations. In the evaluation step, the TWLNI evaluator and the TWSNI evaluator are respectively designed for TWLNI and TWSNI. Two evaluators are designed here, as percentages of interactions of targets with top number of interaction among all interactions are very big. And then the result could be mainly determined by TWLNI when all targets are evaluated together, which could make that the improvement for TWSNI is overwhelmed.

TWLNI classifier and evaluator
A larger number of positive samples can be generated for the TWLNI, and then there would be enough positive samples to predict the interactions of these targets. In this case, because drug-target interactions are very sparsely distributed in the drug-target pair's space, after adding samples of neighbors, much more negative samples than positive samples would be added. And then the effect of predicting DTI for this target may be worsen, which can be shown by the Fig. 3, where Fig. 3a shows the samples of a target and Fig. 3b shows the samples after adding the samples of its neighbors, x is a testing sample of the target, x 1 and x 2 are two positive samples of this target, x 3 and x 4 are two negative samples of its neighbors. It can be seen from the Fig. 3b that many negative samples could be added around the positive samples of this target. As a result, the test sample x could be rightly predicted in the Fig. 3a but be wrongly predicted in the Fig. 3b.
To overcome the above problem, interactions of TWLNI are predicted by using their own positive samples in this paper. Given a training drug feature set D = {d1, d2, . . . , du} ∈ R u×p , training target feature set T = {t1, t2, . . . , tv} ∈ R v×q , and the corresponding interaction matrix Y ∈ R u×v , where u is the number of drugs, p is the number of the drug features, v is the number of targets, and q is the number of target features. To predict the interaction of t j , D can be seen as u samples, Y , j can be seen as the corresponding class label. As a result, the pseudo code of TWLNI classifier can be shown by the Algorithm 1.
x x x 4 x 1

Fig. 3 An example used to show the negative impact of samples of the neighbors
In the step 6 of Algorithm 1, classifier models can be utilized here. However, the number of positive samples is small and the dimension of the extracted feature is high, which should be considered by the utilized classifier model. By analyzing the principles of some classification models, the decision tree has the ability to deal with such problem. The decision tree is generated by a recursive method [36]. In each recursive step, a feature that can gain the most information is used to generate the child node of the decision tree. As a result, the decision tree is influenced by the number of useful features but not the total number of features It can be seen from Algorithm 1 that this algorithm separately trains a classifier for each target. As a result, the evaluation criteria for each target should be also calculated separately. To more easily describe the evaluator, the pseudo code of TWLNI evaluator is shown in the Algorithm 2. It can be seen from the Algorithm 2 that the evaluation criteria result of t j is calculated by the step 4-10, and the mean of evaluation criteria results of all targets is calculated by the step 12.

TWSNI classifier and evaluator
Too few positive samples can be generated for TWSNI. In this case, there are not enough positive samples for this target to predict DTI, so other positive samples should be utilized to improve the effect of DTI prediction. An optional method is to use the positive samples generated by its neighbors.
However, according to the principle of clustering, neighbors of TWSNI would also with smaller number of interactions. As a result, a feature based classifier could be hardly trained in this case. To overcome this problem, a similar based method is used to predict the interactions for these targets. However, because the distributions of drugs and targets are very complex, the similarity calculated by the existing similarity calculation methods could be not good. Specially, the further away the drug or target is, the worse the similarity is. As a result, the nearest profile (NP) [31] is used to improve the DTI effect for TWSNI in this paper.
Given drug similar Sd ∈ R nd×nd , target similar St ∈ R nt×nt , and interaction matrix Y ∈ R nd×nt , where n d and n t are the number of drugs and targets, the interaction Ytnew of a new target tnew can be predicted as following [31]: where tnearest is the nearest target of tnew and Y (:, tnearest) is the interaction of tnearest. The interaction Ydnew of a new drug dnew can be predicted as following [31]: where dnearest is the nearest target of dnew and Y (dnearest, :) is the interaction of dnearest.
Finally, the interaction Y (dnew, tnew) of a drug-target pair (dnew, tnew) can be predicted by mean of their scores.
The method NP is only used to evaluate the DTI effect for TWSNI. To utilize the information offered by their neighbors, all targets are used to calculate Y (dnew, tnew) , as there are not enough positive samples for TWSNI.
After calculating all Y (dnew, tnew) , only the evaluation criteria of TWSNI is output, as the result could be mainly determined by TWLNI, which could overwhelm the improvement for TWSNI. To more easily describe the processing, the pseudo code of TWSNI classifier and evaluator is shown in the Algorithm 3. It can be seen from the Algorithm 3 that the processing is not divided into training processing and testing processing, as the training processing and testing processing of the similar based method are processed in the same time.

Experimental setting
A standard fivefold cross validation is performed and the AUC for each method (i.e. the area under the receiver operating characteristic curve) is computed. More precisely, the drugs are divided into 5 parts, where one part is used for testing and other parts are used for training. For each of the methods being compared, 5 AUC scores were computed (one for each fold) and then averaged to give the final overall AUC score. The AUC score can be biased when the data is imbalanced. However, in this paper, TWLNI and TWSNI are evaluated independently, which means that only targets with similar imbalance are evaluated together. And then imbalance does not affect the effectiveness of AUC for each method. Furthermore, AUC is a good performance evaluation metric for binary classification problem. As a result, AUC is used as the evaluation metrics in this paper.
Many parameters should be set for the compared methods. Parameters of DT, RF, EDT and EKRR used in this paper are the same as that used in the Ref. [19]. Default parameter values were used for DT and MCSDTI as defined in MATLAB's fitctree. The number of trees should be set for RF, which is set to 50. The dimensionality reduction parameter and the number of subspaces should be set for EDT, which are set to 0.8 and 50. The dimensionality reduction parameter, the number of subspaces, the decay term, the Tikhonov regularization parameter, and an adjustable parameter should be set for EKRR, which are set to 0.2, 20, 0.7, 1 and 0.5. The decay term, the Tikhonov regularization parameter, and an adjustable parameter should be set for RLS and RK, which are set to 0.7, 1 and 0.5. NP, WP and NBI do not need to set parameters.
All methods need to extract the drugs features D and targets features T, which can be extracted by the methods described in the subsection "Feature vector extraction". For our method, the experiments results can be obtained by Algorithm 1, Algorithm 2 and Algorithm 3. For the other compared methods, the Y (dnew, tnew) for all testing drugs is firstly calculated by these methods. And then the experiment results for TWLNI are calculated by removing TWSNI and the experiment results for TWSNI are calculated by removing TWLNI.

The experiments for TWLNI
The experiment results are presented in Table 3. These experiments would be used to answer the following questions: (1) Which threshold τ should be set for our method? (2) Is our method better than the compared methods?
As to the first problem, we compare AUCs of the compared methods when the threshold τ is set to 1, 3 and 5, which is given in 3-5 columns in Table 3. Setting τ to different values can show the adaptability of our algorithm. It can be seen from Table 3 that the AUCs of our method are all the best. Specifically, AUCs of our method are respectively 2.47%, 0%, 1.49%, 0.83% and 1.38% higher than that of the second best method when τ = 1 , where the second best method are EDT, WP, EDT, WP and EKRR on NR, IC, GPCR, E and DB. AUCs of our method are respectively 2.32%, 1.48%, 2.41%, 2.65% and 0.87% higher than that of the second best method when τ = 3 , where the second best method are RK, WP, EKRR, WP and EKRR on NR, IC, GPCR, E and DB. AUCs of our method are respectively 1.84%, 2.31%, 2.05%, 2.60% and 0.85% higher than that of the second best method when τ = 5 , where the second best method are EKRR, WP, EDT, WP and EKRR on NR, IC, GPCR, E and DB. It can be seen from the above results that our method is obviously better than the compared methods regardless which value is set to τ and much better than the compared methods when τ is set to 3 and 5.
Furthermore, to better show the results of methods with different τ , the histogram form of Table 3 is given in Fig. 4. It can be seen form Fig. 4 that our method is obviously increased with the increase of τ on NR, IC, E and DB, but most of the compared algorithms have no similar phenomena. The reason may be that more positive samples will be generated with the increase of τ for a target, and then there would be enough positive samples to predict the interactions of this target. As a result, adding samples of neighbors may be worsening for predicting the DTI of this target.
As to the second problem, we will answer it from three aspects. Firstly, it can be seen from Table 3 and Fig. 4 that our method is the best regardless which value is set to τ on all datasets. Specifically, it can be seen from the last column in Table 3 that our method is the best method on all 5 datasets, and the AUCs of our method are respectively 3.31%, 1.27%, 2.02%, 2.02% and 1.04% higher than the second best methods on NR, IC, GPCR, E and DB, where the second best methods are respectively RK, WP, EDT, WP and EKRR. They prove that our method owns the best effect for DTI predicting. Secondly, it can be seen from Table 3 and Fig. 4 that the second best methods are very different on different datasets or by setting different value for τ . It proves that our method is more stable than the compared methods. Thirdly, it can be seen from Fig. 4 that our method is obviously increased with the increase of τ on most datasets, which provides a very good guide to the scope of application of our algorithm. As a result, our method is much better than the compared methods.

The experiments for TWSNI
The experiment results are presented in Table 4. These experiments would be used to answer the following questions: (1) Which threshold τ should be set for our method?
(2) Is our method better than the compared methods?
As to the first problem, we compare the AUCs of the compared methods when the threshold τ is set to 1, 3 and 5, which is given in 3-5 columns in Table 4. It can be seen The maximum and second maximum AUC are shown in bold and italics from Table 4 that the AUCs of our method are the best on NR, IC, E, and the second best on DB when τ is set to 1. However, the AUCs of our method are worse than that of the most compared methods when τ is set to 3 and 5. Specifically, our method is much worse than compared methods when τ is set to 5. Furthermore, to better show the results of algorithms with different τ , the histogram form of Table 4 is given in Fig. 5. It can be seen from Fig. 5 that AUCs of almost all methods are obviously increased with the increase of τ on almost all datasets. However, the increase speed of our method is less than that of other methods. The reason may be that nearest profile is used to improve the DTI effect for TWSNI in this paper and nearest profile could be not very good for targets with a larger number of interactions.
As a result, τ should be set to 1 for our method. Although τ only can be set to 1, TWSNI classifier and TWSNI evaluator are also very useful and important. Firstly, the interaction of targets with a larger number of interactions can be predicted by TWLNI classifier and TWLNI evaluator. It can be seen from Table 3 that TWLNI classifier and TWLNI evaluator can obtain good results when τ is set to 3 and 5. Secondly, TWSNI classifier and TWSNI evaluator own good results when τ is set to 1. It can be seen from Table 4 that our method is the best on NR, IC, E, and the second best on DB when τ is set to 1. Thirdly, the best compared method for TWLNI and TWSNI are not the same, which prove that using different classifier strategies for different targets may be necessary. And then separately designed the TWSNI classifier and TWSNI evaluator for TWSNI is important. It can be seen from Tables 3 and 4 that the best method for TWSNI is RLS, but RLS is not the best method for TWLNI.
As to the second problem, we compare the AUCs of the compared methods when τ is set to 1, as TWSNI classifier and TWSNI evaluator are only used to improve the DTI effect for the targets with smaller numbers of interactions. It can be seen from the second column in Table 4 that our method is the best method on NR, IC and E,  and the AUCs of our method are respectively 1.00%, 3.20% and 2.70% higher than that of the second best methods on NR, IC, and E, where the second best methods are RLS. It shows that our method is better than the compared methods on most datasets. Furthermore, it can be seen from Table 4 that our method is worse than the most of the compared methods on GPCR, the reason may be that NP is used in our TWSNI classifier. NP can consider the problem that the similarity between drugs and the similarity between targets are not very precise, as only the nearest neighborhood is used to predict the DTI. However, this character also makes NP a little sensitive to the nearest neighborhood. As a result, our method can own good results on most datasets but owns bad result on GPCR. So if using our method to predict the DTI for TWSNI, many cross validation on training data should be firstly performance. Actually, most comparison algorithms are prone to the above phenomenon for TWSNI, as the positive samples are not enough for TWSNI. For example, RF is good on NR but bad on other three datasets. EKRR is the best on GPCR but not very good on other three datasets. RK is good on GPCR but not very good on NR and IC. As a result, our method can be also a good method to predict the DTI for targets with a small number of interactions in real applications.

Discussion
Different targets are with very different numbers of interactions and most of the interactions focus on only a few targets. And then some targets could own enough positive samples to predict their interactions but other targets cannot just use their own positive samples to predict their interactions. As a result, for targets that own enough positive samples, the effect of predicting DTI could be worse by adding samples of neighbors, as neighbors could own much more negative samples than positive samples. However, for targets that do not have enough positive samples, many other positive samples should be utilized to improve the effect of DTI prediction. Obviously, the interactions of different targets should be predicted by different methods. Furthermore, another problem is also existed in that different targets are with very different numbers of interactions. If TWSNI and TWLNI are evaluated together, the result could be mainly determined by TWLNI, as most of the interactions focus on only a few targets. However, finding new interactions for TWSNI could be more important than finding new interactions for TWLNI in the real application of the DTI prediction. Obviously, new evaluators should be designed to increase the influence of TWSNI on the results of the experiment.
In this study, MCSDTI is designed according above analyses, which owns following advantages: firstly, interactions of TWLNI and TWSNI are predicted by different classification strategies, which can make better use of the advantages of these classification strategies in different situations, and the information contained in different targets can be more fully utilized. Secondly, TWLNI and TWSNI are evaluated independently, and then the DTI prediction effect of TWSNI can be fairly presented, which provides a new research goal for DTI prediction. It can be seen from Tables 3 and 4 that MCSDTI is much better than the compared methods on most datasets. Specifically, most comparison methods cannot obtain good results for TWLNI and TWSNI in the same and many methods can own a good result for TWSNI but not for TWLNI. They prove that interactions for different targets should be predicted by different methods and all targets cannot be evaluated together.
There are several interesting problems to be investigated in our future work. Firstly, in this paper, an existed method is used to improve the DTI effect for TWSNI. Although this method can play its advantages under our framework, the DTI prediction result is also not very good, and then a better method can be designed for TWSNI in the future. Secondly, a new adaptively MCSDTI framework can be designed, where the number of parts can be adaptively chosen and the threshold used to divide the part can be adaptively set.

Conclusions
This paper presents multiple classification strategies based drug-target interaction (MCSDTI) prediction method. In MCSDTI, targets are firstly divided into TWLNI and TWSNI; and then two classifiers and evaluators are respectively designed for TWLNI and TWSNI to predict the corresponding DTI. As a result, information of different target sets can be better used by different classification strategies; and the evaluation results obtained by different evaluation methods can fairer and more useful. The conducted experiments validate that MCSDTI is a competitive method compared to the previous ones. Most of methods cannot own both good DTI prediction results for TWLNI and TWSNI, but MCSDTI can be much better than the compared methods for both TWLNI and TWSNI on most datasets, which shows that designing different classification strategies for different targets is an effective way to improve the effectiveness of DTI prediction.


Strategies of Epstein-Barr virus to evade innate antiviral immunity of its human host

Epstein-Barr virus (EBV) is a double-stranded DNA virus of the Herpesviridae family. This virus preferentially infects human primary B cells and persists in the human B cell compartment for a lifetime. Latent EBV infection can lead to the development of different types of lymphomas as well as carcinomas such as nasopharyngeal and gastric carcinoma in immunocompetent and immunocompromised patients. The early phase of viral infection is crucial for EBV to establish latency, but different viral components are sensed by cellular sensors called pattern recognition receptors (PRRs) as the first line of host defense. The efficacy of innate immunity, in particular the interferon-mediated response, is critical to control viral infection initially and to trigger a broad spectrum of specific adaptive immune responses against EBV later. Despite these restrictions, the virus has developed various strategies to evade the immune reaction of its host and to establish its lifelong latency. In its different phases of infection, EBV expresses up to 44 different viral miRNAs. Some act as viral immunoevasins because they have been shown to counteract innate as well as adaptive immune responses. Similarly, certain virally encoded proteins also control antiviral immunity. In this review, we discuss how the virus governs innate immune responses of its host and exploits them to its advantage.

Introduction
Epstein-Barr virus (EBV), also referred to as human herpesvirus 4 (HHV-4) is a virus of the Gammaherpesvirinae subfamily with an encapsidated, linear, double-stranded DNA genome of approximately 160 kbs and an envelope of about 180 nm in diameter. More than 95% of the adult population worldwide is infected. Primary infection occurs mainly via saliva and is often asymptomatic in childhood, but is frequently associated with Infectious Mononucleosis (IM), if the first contact is delayed until adolescence or adulthood. IM patients have a higher risk of developing Multiple Sclerosis or malignant diseases later in life (Crawford, 2001;Ascherio and Munger, 2015). After infection, the virus establishes latency in mature B cells and persists in long-lived memory B cells. EBV is associated with certain B cell malignancies such as Hodgkin lymphoma, Burkitt's lymphoma, and post-transplant lymphoproliferative disorder (PTLD) but the virus also causes nasopharyngeal carcinoma and gastric carcinoma in epithelial cells, which EBV can also infect. In immunocompromised individuals, EBV causes PTLD and the majority of acquired immunodeficiency syndrome (AIDS)-defining non-Hodgkin's lymphoma (Yarchoan and Uldrick, 2018). EBV is a critical human tumor virus and a class 1 carcinogen (International Agency for Research on Cancer [IARC], 1997).
Epstein-Barr virus supports its lifelong persistence in us by minimizing the expression of viral proteins to limit viral detection and to escape from antiviral immunity. During EBV's life cycle, three different latent phases are characterized by distinct viral gene expression profiles: latency 0/I [no viral proteins or only EBV nuclear antigen 1 (EBNA1)]; latency III [all latent viral proteins (6 EBNAs, 3 latent member proteins; LMPs)]; and latency II (a subset of latent viral proteins). During latency, up to 44 microRNAs (miRNAs) and two non-coding RNAs (EBERs) are also expressed. In in vitro infected human B cells, various lytic viral genes are expressed initially together with latent EBV genes in the so-called pre-latent phase of infection (Kalla et al., 2010;Kalla and Hammerschmidt, 2012), but lytic gene expression ceases when the incoming EBV DNA becomes epigenetically repressed within a few days.
During latent infection in resting cells, several copies of the EBV genome are stably maintained as fully chromatinized extrachromosomal plasmids which, in proliferating cells, replicate in synchrony with host cell DNA. To allow virus to spread to neighboring cells, reactivation occurs in B cells when they encounter their cognate antigen in vivo (Laichalk and Thorley-Lawson, 2005), but sporadic entry into EBV's productive, lytic phase also occurs spontaneously in latently EBV infected cell lines in vitro. Lytic reactivation is initiated by the viral protein BZLF1, a member of the activator protein 1 (AP-1) family of transcription factors, which induces a massive synthesis of all viral proteins needed for the assembly and egress of virions (Buschle and Hammerschmidt, 2020). The innate immune system is the frontline defense to protect the host from viral infection (Li and Wu, 2021). B cells and epithelial cells that EBV infects but also monocytes-derived macrophages (MDMs), dendritic cells (DCs), and plasmacytoid dendritic cells (pDCs) can sense virion components. These immune cells express a plethora of pattern recognition receptors, PRRs. Different PRRs recognize different viral components: Toll-like receptor 2 (TLR2) senses the viral dUTPAse in monocytes (Gaudreault et al., 2007;Ariza et al., 2009); in monocytes and pDCs and TLR9 senses the unmethylated CpG motifs of virion DNA (Fiola et al., 2010;Bouvet et al., 2021). The EBERs are recognized by TLR3, TLR7, and Retinoic acid-inducible gene I (RIG-I) in different types of established cell lines (Samanta et al., 2006;Iwakiri et al., 2009;Li et al., 2019) but not when EBV infects primary B cells (Bouvet et al., 2021), which is an unexpected finding. EBV is sensed by interferon gamma inducible protein 16 (IFI16) (Ansari et al., 2013) in epithelial cells in which EBV induces TRIM29, a negative regulator of STING (Xing et al., 2017) (stimulator of interferon genes) and an adaptor protein of cytosolic DNA sensors. A STING inhibitor suppressed B cell transformation by EBV (Miyagi et al., 2021) although B cells barely express STING (Mrozek-Gorska et al., 2019). Further research is needed to clarify its role in EBV infection.
Despite the known innate immune responses EBV establishes long-term latency in most of us, suggesting that it has evolved to escape from human immune recognition. EBV uses several viral gene products with immune evasive functions in different phases of the viral life cycle since many viral components are antigens and are recognized by the host. We summarize recent advancements in understanding the diverse classes of molecules that EBV uses to evade innate immune responses.

BPLF1
BPLF1 is a large (3149 aa) EBV protein expressed during EBV's lytic phase and contained in the tegument of virions. BPLF1 stabilizes RAD18 ubiquitin complexes and acts as a deneddylase (removing the ubiquitin-like protein NEDD8) important for lytic viral DNA replication and production of infectious virions (Gastaldello et al., 2010;Kumar et al., 2014). Infection of humanized mice with a BPLF1-knockout virus showed reduced infectivity and delayed B cell transformation suggesting that BPLF1 contributes to but is not essential for the virus to spread in vivo (Whitehurst et al., 2015). The N-terminal region of the protein contains a deubiquitinating (DUB) domain, which interacts with proliferating cell nuclear antigen (PCNA) to reduce the cellular DNA damage response after lytic reactivation (Whitehurst et al., 2012). The DUB domain also reduces immune recognition during lytic activation by removing polyubiquitin chains from various components of TLR signaling, including NEMO, IKKα, and TRAF6 (Saito et al., 2013;van Gent et al., 2014). BPLF1 was further reported to interact with the 14-3-3 protein and the E3 ligase tripartite motif-containing protein 25 (TRIM25) mitigating type I interferon (IFN)-mediated responses by sequestering TRIM25 into a trimeric complex. Since RIG-I's functions depend on ubiquitination by TRIM25, its inhibition causes the loss of the ubiquitin scaffold on RIG-I and affects successive Frontiers in Microbiology 02 frontiersin.org downstream signaling cascades (Gupta et al., 2019). The decreased NF-κB activation in response to EBV reactivation reduces pro-inflammatory cytokine secretion levels. BPLF1 may also have an immune suppressive role immediately after B cell infection since BPLF1 contained in EBV's infectious particles can control TLR signaling (van Gent et al., 2014) and BPLF1 is also transiently expressed during EBV's pre-latent phase (Mrozek-Gorska et al., 2019).

EBV nuclear antigen 1
EBV nuclear antigen 1 is a homo-dimeric protein expressed in all types of viral latency but latency 0. EBNA1 has crucial functions to maintain EBV genomes stably, to replicate viral DNA, and to partition the newly replicated EBV genomes to daughter cells. EBNA1 binds to the EBV origin of plasmid replication (oriP), which is critical for maintaining and replicating the viral genome and for its partitioning during mitosis (Chiu and Sugden, 2018). EBNA1's aminoterminus binds cellular chromatin to localize EBV genomes to perichromatin and contains a glycine alanine repeat (GAr) domain with immune evasive function reducing presentation of EBNA1 epitopes to CD8 + cytotoxic T cells (Blake et al., 1997;Yin et al., 2003).
EBNA1's role in regulating innate immunity is less clear. It interferes with functional NF-κB activation in different carcinoma cell lines (Valentine et al., 2010) and it can contribute to the development of EBV-associated tumors and their progression by activating AP-1 and signal transducer and activator of transcription 1 (STAT1) (O'Neil et al., 2008) and inhibiting TGF-β1 (Wood et al., 2007;Flavell et al., 2008). This is counterintuitive because STAT1 activation typically induces strong anti-viral IFN responses. EBV suppresses these "adverse effects" together with other immunoevasins such as EBNA2 or viral miRNAs. EBNA1 also induces CXCL12 secretion to recruit regulatory T cells and to create an immunosuppressive microenvironment (Huo et al., 2020). Finally, EBNA1 also regulates the expression of NKG2D ligands which reduces the recognition of EBV infected cells by NK cells shortly after infection (Westhoff Smith et al., 2021).

EBNA2
EBNA2 acts as the main transcriptional transactivator (Kempkes and Ling, 2015) and is essential for the reprogramming and transformation of primary B cells infected by EBV in vitro (Hammerschmidt and Sugden, 1989). EBNA2 induces a plethora of cellular genes including MYC  and genes coding for cell surface and adhesion molecules such as CD21 and CD23, but EBNA2 also activates multiple viral proteins including EBNA1, LMP1, and LMP2 (Kempkes and Ling, 2015). Regarding EBNA2's role in innate immunity, it induces the expression of the IL-18 receptor (IL-18R) in primary B cells (Pagès et al., 2005). IL-18R may function differently depending on its ligands, the pro-inflammatory and anti-inflammatory cytokines IL-18 and IL-37, respectively. With IL-37, IL-18R can inhibit innate immune responses in general (Nold-Petry et al., 2015) but the contribution of the IL-18R pathway to EBV's strategy of immune evasion needs to be assessed. EBNA2 was also shown to upregulate TNF-α and LT-α to support cell proliferation (Spender et al., 2001). EBNA2 also regulates host miRNAs hsa-miR-21 and miR-146a that act as negative feedback of type I IFN responses in the RIG-I pathway (Rosato et al., 2012). Already in the 1990s, EBNA2 was described to render the infected cells insensitive to IFN (Aman and von Gabain, 1990) and to suppress interferon stimulated genes (ISGs) but EBNA2 also induces low levels of IFN-β suggesting that chronic low levels of IFN may desensitize cells and dampen proper anti-viral responses (Kanda et al., 1992(Kanda et al., , 1999. In 293T and HeLa cells, EBNA2 activates STAT3, which could provide a growth advantage to EBV infected cells (Muromoto et al., 2009).

BCRF1 (vIL-10)
Epstein-Barr virus also manipulates innate immunity using viral IL-10 (vIL-10) encoded by the BCRF1 gene of EBV, a viral homolog of human IL-10 (Sin and Dittmer, 2012). vIL-10 is an anti-inflammatory cytokine that suppresses secretion of pro-inflammatory cytokines such as IFN-γ and IL-2 from EBV infected PBMCs (Jochum et al., 2012a). NK cells can sense and lyse EBV infected cells particularly during EBV's lytic phase. CD4 + T cells can enhance NK cell activity, but vIL-10 suppresses this adjuvant T cell effect reducing the cytotoxic activity of NK cells (Jochum et al., 2012a). vIL-10 also controls adaptive immunity by suppressing TAP1, class I HLA, and co-stimulatory molecules such as ICAM-1 (Zeidler et al., 1997;Salek-Ardakani et al., 2002). Unlike hIL-10, vIL-10 does not induce class II MHC (Go et al., 1990) or promotes thymocyte proliferation (MacNeil et al., 1990) probably due to its lower receptor affinity compared to hIL-10 (Liu et al., 1997). In a recent study, vIL-10 reduced phosphorylation of STAT3 and vIL-10-treated monocytes showed weaker phagocytosis of apoptotic cells compared to hIL-10-treated cells (Jog et al., 2018). The impaired activity of monocytes to clear dying cells may lead to reduced antigen presentation indirectly supporting EBV infection in vivo. vIL-10 is not only a lytic gene but is markedly expressed in the pre-latent phase of B cell infection (Jochum et al., 2012a;Sin and Dittmer, 2012). Mice infected with a strain of murine gammaherpesvirus 76 engineered to encode EBV's vIL-10 showed higher virus titers in the lung compared to wild-type virus but vIL-10 had no effect on virus reactivation or the number of latently infected splenic cells (Lindquester et al., 2014). Thus, vIL-10 can also contribute to a pro-viral microenvironment early after infection in this mouse model.

Latent membrane protein 1
Latent membrane protein 1 contains 6 transmembrane domains and a C-terminal signaling domain and is located mainly in intracellular membranes (Lam and Sugden, 2003). LMP1 mimics a constitutive, i.e., ligand-independent CD40 receptor and its downstream signaling cascade (Kilger et al., 1998;Kieser and Sterz, 2015) contributing to survival and proliferation of EBV infected B cells (Wang et al., 1985;Zimber-Strobl et al., 1996;Kilger et al., 1998). LMP1 drives an innate anti-viral response but it also evokes an anti-viral state in infected cells. LMP1 induces interferon regulatory factor 7 (IRF7) via TRAF6 and RIP mediated ubiquitination (Zhang and Pagano, 2000;Huye et al., 2007;Ning et al., 2008) and activates the JAK/STAT pathway as well as both canonical and non-canonical NF-κB signaling pathways (Gires et al., 1999;Huye et al., 2007). As a consequence, EBV infected cells release type I and II interferons leading to an upregulation of ISGs including ISG15, STAT1, and 2 ,5oligoadenylate synthetase (OAS) (Richardson et al., 2003;Najjar et al., 2005). Using LMP1 mutants, STAT1 regulation was shown to depend on both signaling domains of LMP1, CTAR1, and CTAR2 . The authors suggested that this anti-viral response can be advantageous for the virus because it also supported latent infection in their experimental model . Of note, most of these reports rely on latently EBV infected, established B cell lines.
Recent reports identified additional immune evasive functions of LMP1. LMP1 downregulated TLR9 and reduced the recognition of non-CpG methylated viral DNA (Fathallah et al., 2010). Later, newly EBV infected B cells were found to be rather inert to sensing viral DNA (Bouvet et al., 2021), but plasmacytoid dendritic cells (pDCs) released high levels of type I IFN upon encounter with viral DNA and its detection by TLR9 (Bouvet et al., 2021). In 293T cells, LMP1 expression promoted RIG-I degradation (Xu et al., 2018), which might also dampen antiviral responses.

Latent membrane protein 2
The viral LMP2 gene encodes two proteins, LMP2A and LMP2B, which differ in their N-terminal domains. Both LMP2A and LMP2B are integral transmembrane proteins embedded in the plasma membrane with their 12 transmembrane spanning domains. LMP2A's N-terminal domain is cytosolic and contains two proline-rich PY domains as well as an immunoreceptor tyrosine-based activation motif (ITAM). The ITAM motif interacts with signal mediators of the B-cell receptor (BCR), mimics an active BCR (Engels et al., 2001a,b) and maintains a "tonic" B cell receptor-like signal (Mancao et al., 2005) and references therein. Concomitantly, LMP2A contributes to B cell survival as its expression supports viral latency and prevents acute BCR triggering (Brielmeier et al., 1996;Merchant et al., 2000;Mancao and Hammerschmidt, 2007). LMP2B has a short, 19 amino acid long N-terminal domain, only, which is incapable of signaling and was shown to form heterocomplexes with LMP2A regulating LMP2A's activity (Lynch et al., 2002). LMP2A also controls adaptive immunity by downregulating CIITA, the master regulator of HLA class II alleles, reducing epitope presentation via HLA class II (Lin et al., 2015). LMP2A not only reduces immune recognition by CD8 + T cells (Rancan et al., 2015) but also innate immune responses in epithelial cell lines. IL-6 expression and secretion from carcinoma cell lines was suppressed by wild-type EBV but not by an LMP2A-deleted mutant EBV indicating that LMP2A shuts off this inflammatory cytokine (Stewart et al., 2004). LMP2A also downregulated LMP1, suggesting a fine-tuning of LMP1's activities in EBV infected cells (Stewart et al., 2004) probably mediated by TRAF2 (Guasparri et al., 2008). Because LMP2A can induce the degradation of the receptors IFNAR1 and IFNGR1, LMP2A affects expression of ISGs and thus reduces type I and type II interferon responses in epithelial cells (Shah et al., 2009). Interestingly, LMP2B was also found to contribute to IFN receptor degradation suggesting that the N-terminal cytosolic tail of LMP2A, which LMP2B lacks is not important for this ubiquitin-dependent mechanism (Shah et al., 2009).

Epstein-Barr virus microRNAs
Epstein-Barr virus encodes 44 mature miRNAs which are important for cell survival and proliferation in the pre-latent phase of B cell infection (Seto et al., 2010;Feederle et al., 2011). Recently, many viral miRNAs were also found to govern innate and adaptive immune responses in vitro (Albanese et al., 2016;Tagawa et al., 2016;Bouvet et al., 2021) and in humanized mice in vivo (Murer et al., 2019). miRNAs regulate important proteins in different pathways of innate immunity. EBV miR-BART15 targets the NLRP3 (NOD-, LRR-, and pyrin domain-containing protein 3) inflammasome and EBV mir-BHRF1-2 was shown to regulate the IL-1 receptor preventing IL-1β release and downstream activation (Haneklaus et al., 2012;Skinner et al., 2017). EBV miR-BART2 targets MICB facilitating immune escape from Natural Killer (NK) cells (Nachmani et al., 2009). EBV miRNAs in primary B cells target key components of the interferon pathway reducing production and release of IFN from infected cells as well as autocrine and paracrine IFN mediated responses affecting ISG-driven transcription (Bouvet et al., 2021). Additional targets involved  in reducing type I IFN response are CBP (CREB-binding protein) (Hooykaas et al., 2017) and RIG-I (Lu et al., 2017). Interestingly, pDCs, although they are only abortively infected, sense EBV's genomic DNA in a TLR9 dependent manner but EBV's miRNAs seem to counteract this innate response. The mode of action is not completely clear but could be mediated by miRNAs contained in virions (Jochum et al., 2012b;Bouvet et al., 2021). miRNAs are particularly interesting as immunoevasins. Viral proteins including those with anti-inflammatory functions are immunogenic and thus can stimulate adaptive immunity but viral miRNAs escape from immune recognition entirely.

Epstein-Barr virus lytic proteins
In latently infected cells certain viral genes can control innate immunity, but lytic genes also undermine immune functions. IRF7 was shown to be regulated by the immediate early genes BZLF1 (Hahn et al., 2005) and BRLF1 (Bentz et al., 2010). Similarly, several lytic proteins, BGLF4 (Wang et al., 2009), BRLF1 (Bentz et al., 2010), BHRF1 (Vilmen et al., 2021), and BFRF1  target IRF3. It and IRF7 are critical for type I IFN production and downstream ISG signaling, suggesting that the orchestrated suppression of both IRF3 and IRF7 is important in EBV's lytic, productive phase. BGLF2 was identified to control all three types of IFN (α, β, ε) as it interferes with STAT1/STAT2 (Jangra et al., 2021) and tyrosine kinase 2 (TYK2) functions . Viral lytic genes also target PRRs. The BGLF5 exonuclease downregulates TLR2 (van Gent et al., 2015) and the dsDNA sensor TLR9 (van Gent et al., 2011). BLRF1 not only inhibits IRF3 but also suppresses transcription of RNA polymerase III-dependent immunogenic small RNAs dampening the RIG-I pathway (Long et al., 2021). Secreted BARF1 is a decoy receptor of macrophage colony stimulating factor (M-CSF) interfering with macrophage activation (Hoebe et al., 2012). Immune evasion appears to be particularly important during latent infection as compared to EBV's lytic phase, when viral host shut-off mechanisms compromise cellular protein synthesis and thus immune functions (Rowe et al., 2007;Buschle et al., 2021). But, as in the case of BPLF1, viral genes that have been previously deemed lytic genes are now found to be expressed during EBV's pre-latent phase of infection as well. These lytic immunoevasins are, therefore, potentially functional upon de novo infection.

Discussion
The data discussed above and summarized in Table 1 and Figure 1 show that EBV has evolved to encode different classes of immune evasins, which contribute to successful infection and stable latency in its human host. Many factors have been found to play various but likely crucial roles in evading innate immunity and probably more factors and new principles will be discovered. Moreover, it appears difficult to assess the importance and contribution of each individual immunoevasin detected or claimed in the literature. Few findings have been independently confirmed or falsified. This uncertainty is partly due to the many different experimental systems and models used by different groups working in this field of virology and antiviral immunity.
Use of models that closely mimic in vivo infection is a potential solution to this problem. We recently showed that primary human B cells constitute a valid model to assess the importance of candidates for immune evasion in the different phases of EBV infection. Using genetic EBV mutants that differ only in single genes are also supportive. For example, we found that the EBV gene BILF4 (LF2), which was shown to play a role in restricting innate immune response (Wu et al., 2009), did not show any reduction in IFN response in freshly infected B cells (Bouvet et al., 2021). This result suggested that data obtained from phenotypic studies in established cell lines are not necessarily relevant in models recapitulating critical steps in infection of EBV's genuine target cells. A better understanding of the virus and its crucial factors that are responsible for successful infection will be important to identify potential therapeutic targets to prevent or cure EBV-associated diseases.

Author contributions
MA, TT, and WH conceptualized the topic. MA and TT researched and analyzed the literature, and drafted the manuscript including interpretations. WH analyzed the background literature, and wrote and finalized the manuscript together with MA and TT. All authors approved the final version of the manuscript, ensured the accuracy and integrity of the work, and agreed to be accountable for all aspects of the work.


Genetically Encoded Whole Cell Biosensor for Drug Discovery of HIF-1 Interaction Inhibitors

The heterodimeric transcription factor, hypoxia inducible factor-1 (HIF-1), is an important anticancer target as it supports the adaptation and response of tumors to hypoxia. Here, we optimized the repressed transactivator yeast two-hybrid system to further develop it as part of a versatile yeast-based drug discovery platform and validated it using HIF-1. We demonstrate both fluorescence-based and auxotrophy-based selections that could detect HIF-1α/HIF-1β dimerization inhibition. The engineered genetic selection is tunable and able to differentiate between strong and weak interactions, shows a large dynamic range, and is stable over different growth phases. Furthermore, we engineered mechanisms to control for cellular activity and off-target drug effects. We thoroughly characterized all parts of the biosensor system and argue this tool will be generally applicable to a wide array of protein–protein interaction targets. We anticipate this biosensor will be useful as part of a drug discovery platform, particularly when screening DNA-encoded new modality drugs.

■ INTRODUCTION
HIF-1 is a heterodimeric transcription factor composed of HIF-1α and HIF-1β subunits that activates expression of genes for angiogenesis, glucose metabolism, cell proliferation, and metastasis in response to hypoxia. 1 HIF-1 is overexpressed in many cancers, leading to intratumoral adaptation to hypoxia and resistance to cancer treatments. 1 Manipulation of HIF-1 expression has shown beneficial effects on tumor growth, making it a good target to drug in combination therapies. 2 Nevertheless, few traditional small molecule drug discovery efforts have provided leads against HIF-1 dimerization, 3 let alone generated clinically available drugs. 4 The importance of HIF-1 in cancers, compounded with difficulties in finding specific inhibitors, provides a strong impetus to explore alternative drug discovery approaches to use against HIF-1.
New modality therapeutics, such as peptides, RNA therapeutics, protein degraders and antibody conjugates offer an innovative solution against hard to hit targets, such as protein−protein interactions (PPIs) like HIF-1. 5 Discovery of these biology-based therapeutics can be supported by synthetic biology approaches, where living systems are engineered to not only produce new chemical diversity but also to discern functionality using genetic screens. 6 While assays for drug discovery against HIF-1 have been engineered in different mammalian cell lines, 7,8 their use is limited when compared to implementations in simpler organisms such as bacteria or yeasts that can also be efficiently engineered to display tens of millions of new modality therapeutic variants. For example, engineered bacteria were used to screen libraries of genetically encoded cyclic peptides against a variety of PPI targets, including HIF-1. 9−11 The yeast Saccharomyces cerevisiae has advantages over bacteria as a drug discovery tool. It is evolutionarily closer to humans, meaning any finding should translate better into the clinic; 12 yeast can glycosylate proteins, which enables it to mimic human targets with better fidelity; 13 and efforts are underway to humanize yeast. 14 Yeast can also produce a wide array of compounds, and many characterized genetic parts can be combined to screen a variety of molecules for drug leads. 6 Testament to the power of yeast, it is the archetypical system for studying PPIs using the yeast two-hybrid system (YTH). 15 Here, if two proteins interact, they induce the expression of a reporter gene by creating an artificial transcription factor. Similarly, the HIF-1 transcription factor has been expressed in yeast and shown to activate a reporter gene downstream of its cognate binding motif. 16 While this assay was used to identify HIF-1 effectors, it is neither versatile nor robust. It cannot be adapted to investigate other PPIs as it requires cognate HIF-1 transcriptional activation and DNA binding. More fundamen-tally, the YTH system is not suited to discover PPI inhibitors as it lacks a positive selection, as desired compounds do not cause reporter gene expression.
A clever variation of the yeast two-hybrid system, the repressed transactivator (RTA) yeast two-hybrid biosensor system, was engineered as a drug discovery screen to identify small molecule protein interaction inhibitors. 17,18 Here, when two proteins interact, they repress the expression of a reporter gene. The reporter gene is only then expressed if a compound prevents the PPI, thus providing a positive selection for PPI inhibition and making the selection for potential false positives less likely. The RTA system however relies on growth as readout, which is difficult to precisely quantify, thus making ranking of relative drug potency inaccurate. Furthermore, no mechanisms to control for drug off-target effects are in place, leading to potential false-positives that could quickly overgrow a culture if growth is used as selection. Addressing these issues should provide a more robust assay, thus reducing potential drug candidate attrition.
We wanted to expand the RTA system as a versatile yeastbased drug discovery tool and then validate it using HIF-1 as the target. To do this, we explored the use of fluorescence as reporter output to enable fluorescent activated cell sorting (FACS) to be used in conjunction with growth selection, which is more suitable as a prescreening step. Additionally, we thoroughly characterized all individual parts in the sensor, and where possible, we used orthogonal systems to those endogenous to yeast. Furthermore, we engineered an internal control for biosensor cell health and an additional control strain to detect false positives. Using our optimized RTA system, we show fluorescence output and growth selection for the biosensor strain when HIF-1α/HIF-1β dimerization is inhibited, establishing this new tool as an important element in future drug discovery platforms.

Design and Construction of a Protein−Protein
Interaction Inhibitor Biosensor with Fluorescence Output. A schematic describing the expanded RTA system designed in this study specifically to detect HIF-1α/HIF-1β dimerization inhibitors is found in Figure 1. Briefly, a transcriptional activator fused to a bait protein (HIF-1α) will induce expression of a reporter gene when targeted to it via DNA binding domains. When a prey protein (HIF-1β) is coexpressed as a fusion to a transcriptional repressor, it will turn off the reporter gene through bait−prey interaction. The reporter gene is only then turned back on when a compound (e.g., small molecule) inhibits the bait−prey PPI. Details describing specific parts redesigned in this study are outlined in the following sections.
We chose the enhanced green fluorescent protein (EGFP) 19 as a FACS-compatible reporter gene. Additionally, we chose the bacterial DNA-binding protein LexA 20 to interact with the reporter gene promoter as an orthogonal solution to the GAL system used in the previous setup. 17 Using these parts, we explored whether a genetic configuration could lead to high reporter gene expression, yet would also be sensitive to transactivation. We genomically integrated cassettes with either two, four, or eight lexA operators 21 upstream of the minimal CYC1 promoter (pCYC1min) to drive EGFP expression (lexAx2-pCYC1min-EGFP, lexAx4-pCYC1min-EGFP, or lexAx8-pCYC1min-EGFP). In these strains we also coexpressed a LexA-based 22 transactivator comprised of the full length LexA repressor which binds the lexA operators upstream of the reporter gene, the SV40 nuclear localization signal (NLS) 23 to ensure transport of the fusion into the nucleus, and the strong transcriptional activator VP16. 24 The transactivator cassette was also genomically integrated and expressed from the constitutive ADH1 promoter. GFP fluorescence was measured by flow cytometry to evaluate reporter gene activation ( Figure  2A). EGFP expression was seen only in cells coexpressing the LexA-NLS-VP16 fusion and increased with number of lexA binding boxes. However, the intensity of the fluorescence induction saturated when more than four lexA boxes were present. These results show the reporter gene with four operator sequences is the best performer when considering both fluorescence output and responsiveness to the transactivator.
We subsequently investigated if the transactivator could still activate gene expression as a tripartite fusion displaying a bait protein. We also explored reporter gene expression when the transactivator−bait fusion was expressed from weak or strong promoters characterized in the modular cloning (MoClo) yeast tool kit (YTK). 25 Additionally, we tested the use of a different transcriptional activator; while VP16 is a powerful transcriptional activator, 21 VPR, which is a fusion of three different activation domains (VP64, p65, and Rta38), is considerably stronger. 26 We integrated cassettes expressing either LexA-NLS-VP16 or LexA-NLS-VPR fused to the bait HIF-1α and measured their ability to activate EGFP (lexAx4-pCYC1min-EGFP) expression via flow cytometry. We found that despite fusion of the bait protein, transactivators containing either VP16 or VPR provided a strong fluorescence signal ( Figure  2B). Furthermore, the VPR activator was strong enough to induce a signal even when expressed from the weakest promoters in the YTK collection (i.e., pREV1). As the biosensor should be most responsive to inhibitors when its parts are least abundant, 17 we continued biosensor testing with the strain expressing the transactivator−bait fusion from the REV1 promoter.
We next investigated if a PPI could recruit a transcriptional repressor to our reporter gene, thus turning it off. As HIF-1 is a heterodimer of HIF-1α/HIF-1β, we expressed HIF-1β as prey while fused to an NLS and either of the transcriptional repressors: Tup1 (N-terminal repression domain), 18,27 Mig1, 28 or Mxi1 (N-terminal repression domain). 29,30 We expressed these repressor fusions from either the RNR2 or REV1 promoters in the reporter strain (lexAx4-pCYC1min-EGFP) coexpressing the transactivator−bait (LexA-VPR-HIF-1α) from the REV1 promoter. When measuring reporter gene fluorescence by flow cytometry (Figure 2C), we found Tup1 to provide the best transcriptional repression, and that repression increased with repressor−prey expression. These results indicate that repression of reporter gene expression is caused by bait−prey interaction.
Validation of the Fluorescent RTA Biosensor. From our results characterizing the reporter gene, transactivator− bait, and repressor−prey fusions, we deemed a system with four lexA operators (lexAx4-pCYC1min-EGFP), expressing the VPR transactivator-HIF-1α fusion from the REV1 promoter, and the Tup1 repressor-HIF-1β fusion from the RNR2 promoter would be a suitable biosensor configuration. With this configuration, the biosensor is in a repressed "OFF" state and only outputs 14% of the maximal fluorescence when compared to reporter gene transactivation solely from LexA-VPR-HIF-1α ( Figure 2D). We next needed to validate if output is shown as maximal reporter gene (lexAx4-pCYC1min-EGFP) output (AD-HIF-1α), repressed "OFF" state output (AD-HIF-1α + RD-HIF-1β), and derepressed "ON" state output (AD-HIF-1α + RD-HIF-1β + HIF-1β). Biosensor derepression is achieved when an additional HIF-1β subunit is coexpressed. Additionally, fluorescence output from the reporter gene lexAx4-pCYC1min-EGFP in cells coexpressing the transactivatorrepressor fusion (AD-RD) from the REV1 promoter is shown as a midway "MID" state. The output of the sensor in either "OFF", "ON", or "MID" states is shown as a percentage of the original biosensor fluorescence. (E) Time course study of the dynamic range of the biosensor. Fluorescence output relative to biomass for strains described in panel D was measured over time. Bars represent mean values, and error bars represent the standard deviation. (F) Fluorescence (GFP) output of the biosensor reporter gene (lexAx4-pCYC1min-EGFP) relative to constitutive miRFP670 expression. GFP fluorescence of the biosensor strain in the "OFF" state (AD-HIF-1α + RD-HIF-1β) increases relative to miRFP670 fluorescence in the "ON" state when coexpressing an additional HIF-1β subunit (AD-HIF-1α + RD-HIF-1β + HIF-1β). For panels A-D, fluorescence was measured by flow cytometry after 16 h of culture; bars represent mean values, and error bars represent the range. For panels D−F, AD = LexA-VPR, RD = Tup1, and AD-RD = LexA-VPR-Tup1. ACS Synthetic Biology pubs.acs.org/synthbio Letter inhibition of HIF-1α/HIF-1β dimerization could be registered as an increase in fluorescence output from this "OFF" state. We expressed an additional HIF-1β subunit from a centromeric plasmid using the TEF1 promoter in this biosensor strain. We observed derepression of the reporter gene by flow cytometry, presumably caused by competitive binding of the additional HIF-1β subunit to the transactivator-HIF-1α ( Figure 2). The sensor in this "ON" state returned to 95% of the maximal fluorescence output when compared to reporter gene transactivation solely from LexA-VPR-HIF-1α ( Figure 2D). As the HIF-1α/HIF-1β Per-Arnt-Sim (PAS) B domain interaction K D is 378 nM, 31 the sensor should therefore be sensitive enough to detect inhibitors close to this potency.
Our previous results imply that the reporter gene is sensitive to the interplay between transactivator−bait and repressor− prey expression. As these parts are driven by endogenous promoters that may up-or down-regulate throughout different growth stages in a batch cultivation, we did a time course study to measure the fluorescence response of the biosensor to an additional HIF-1β subunit. We found the biosensor to be most responsive from 6 to 18 h, during which the largest differences in fluorescence between "ON" and "OFF" states were recorded ( Figure 2E). Overexpression of the additional HIF-1β subunit caused a slight growth defect ( Figure S3); however, this did not translate into lower relative fluorescence ( Figure S4).
It is possible that reporter gene expression in the biosensor is repressed through overexpression of Tup1 and not because of recruitment of the repressor−prey protein. To investigate this, we replaced wild-type HIF-1α with a dimerization weak mutant, HIF-1α mut (Q320E, V336E, and Y340T), 32 as transactivator−bait fusion in the biosensor strain. This transactivator should only weakly recruit the repressor−prey fusion, resulting in less reporter gene repression. We found the biosensor with mutant HIF-1α displayed significantly less reporter gene repression (39% of the maximal output) when compared to the wild-type transactivator-HIF-1α strain (14% of the maximal output) (Figures S1 and 2D). We next removed the HIF-1α bait from the transactivator fusion in the biosensor strain (while still retaining expression of the repressor-HIF-1β fusion). Although reporter gene expression decreased slightly (only down to 71% of the maximal output) in this variant, this decrease was significantly less than observed when both interacting bait and prey fusions were present (down to 14% of the maximal output) (Figures S2 and 2D). Together, these results indicate reporter gene expression is predominately dependent on PPI and that inhibition of PPI can be selected for using fluorescence.
Biosensor Controls for Biological and off-Target Effects. The biosensor output could be affected by biological distortions related to cell size and growth, or by off-target and pleiotropic effects from screened drugs. To mitigate this, we integrated a cassette expressing miRFP670 33 under control of the constitutive TEF1 promoter to provide a red fluorescence signal to normalize the GFP output of the biosensor strain with. Derepression of this biosensor from "OFF" to "ON" ACS Synthetic Biology pubs.acs.org/synthbio Letter states, achieved by coexpressing an additional HIF-1β subunit, is observed as an increase in GFP expression relative to miRFP670 ( Figure 2F). In parallel, we engineered an additional biosensor strain to counter-screen against false-positive signals that could arise if a drug has off-target effects, for example by binding to the repressor and inactivating it. In this strain, the proteininteracting partners were omitted, leaving a fusion of LexA-VPR-Tup1. When recruited to the lexA-controlled reporter gene, the signal strength is midway "MID" between the fully activated and fully repressed biosensor, achieving only 37% of the maximal reporter gene output ( Figure 2D). Here, if a drug inactivates the repressor, thus giving a false positive signal in the primary screen, we should be able to discern this as an increase in fluorescence.

Design and Construction of a Protein−Protein Interaction Inhibitor Biosensor with Auxotrophic
Selection. The original RTA sensor system uses auxotrophy as selection, a powerful way to quickly screen large libraries without limitations of FACS throughput. Unfortunately, auxotrophic output is generally binary, where cells grow or not, making the relative assessment of selected inhibitor potency difficult. We reasoned instead that auxotrophic selection would be better suited as a prescreening step used in conjunction with FACS to decrease the number of library members before more detailed investigation of potency via fluorescence.
To take advantage of growth selection as a prescreen step, we integrated a cassette expressing HIS3 from the minimal CYC1 promoter controlled by two lexA operators (lexAx2-pCYC1min-HIS3) into histidine auxotrophic yeast strains also engineered with various parts of the biosensor (combinations of reporter gene, transactivator−bait, and repressor−prey). We found the strain expressing the HIS3 reporter, additional to the EGFP reporter and the transactivator−bait, grew similarly to the strain also coexpressing the repressor−prey, indicating that the HIS3 reporter gene was not repressed enough to prevent growth in media lacking histidine ( Figure 3A).
We made additional variations of the HIS3 reporter gene in an effort to lessen leaky expression based on strategies to decrease transcription and/or translation. 34 First, we retained the two lexA boxes in the reporter gene promoter region but replaced the native HIS3 ATG start codon with the rare TTG variant (lexAx2-pCYC1min-ttg-HIS3). We also engineered a reporter gene with only a single lexA operator and mutated the Kozak consensus region 35 to hexa-cytosine (lexAx1-pCYC1min-cccccc-HIS3). We found the strain with the TTG start codon could not survive in media lacking histidine when only the transactivator−bait was coexpressed, indicating HIS3 expression was too low in this variant ( Figure 3C). We found however that the variant with one lexA box and the deoptimized Kozak sequence grew in media lacking histidine when only coexpressing the transactivator−bait yet did not survive when also coexpressing the repressor−prey ( Figure  3B). This result suggests that the HIS3 gene is repressed enough to allow selection for HIF-1α/HIF-1β dimerization inhibitors.
To validate the auxotrophic selection, we expressed an additional HIF-1β subunit from a centromeric plasmid using the TEF1 promoter in the biosensor strain also harboring the lexAx1-pCYC1min-cccccc-HIS3 reporter gene. We observed derepression of both fluorescent and auxotrophic reporter genes ( Figure 3D,E), indicating both selection mechanisms were functional within the same strain.

■ DISCUSSION
As protein-based new modality drugs can be DNA encoded, it stands to reason to use cells as living factories to produce them, enabling the display of tens of millions of drug variants simply by expressing a DNA library. However, this strategy is limited by traditional high-throughput screening protocols that can assay only hundreds of thousands of variants. A solution is to use an internal selection mechanism within every drugproducing cell, enabling the producer cell to also decipher whether it has made a functional drug variant. Such a selection would need to meet throughput demands yet also provide a sensitive readout related to potency. This work presents an addition to the genetic selection toolbox intended to address these issues.
Fundamental to any engineering discipline, the biosensor presented is modular, easy to engineer, allows rapid prototyping, and behaves in a predictable manner. We designed and built the optimized RTA biosensor with a plug-and-play mindset by adopting the yeast modular cloning toolkit assembly method. 25 Biosensor parts can be easily combined to express many target variants, at differing expression levels, when fused to transcriptional activators and repressors of choice. Furthermore, recent toolkit developments facilitate these constructs to be directly integrated into desired genomic locations. 36 We also observed a clear relationship between part and function (e.g., transcriptional activator strength and fluorescence signal), giving predictability to the system as well as future-proofing it. Our system should therefore be open to new parts as they become characterized. Future work should explore other transcriptional repressors, possibly by combining repression domains, 37 as we did not see strong repression using Mxi1 or Mig1 as reported elsewhere. 28,29 More efficient repression of the system would provide a larger dynamic range. Ultimately, statistical modeling could inform part choice for finely tuned sensors intended for specific applications. 38 The biosensor provides an output that is stable, tunable, able to be interpreted easily with general laboratory methods, and with high dynamic and operational range. It could discern between strong and weak interacting partners, making the system a useful way to select for relative drug potency. While the biosensor could detect a nM binder (HIF-1β subunit PASB domain), it was not fully activated (albeit close to 100%), suggesting the limit of detection may be lower. Future work should relate binding kinetics with sensor output 39 to better gauge sensor sensitivity. Additionally, existing cyclic peptide inhibitors with known potency against HIF-1 should be expressed in the sensor strain to ascertain limits of detection 10 and would be an important first step should this sensor be used to display peptide variants. Ultimately this assay is more intended as a primary screen from which the most potent hits would be channeled into a secondary biophysical assay for further characterization.
The sensor is also stable, likely owing to the genomic integration of all biosensor parts, whereas previous iterations mostly used episomal expression. 17 Furthermore, it has a large operational window, important as an inhibitor made in vivo may need time to accumulate within the cell to a functional concentration. Care should be taken to express any genetically encoded modalities using a promoter similar to, or stronger ACS Synthetic Biology pubs.acs.org/synthbio Letter than, the TEF1 promoter (used to express the HIF-1β subunit during validation) to ensure accumulation of therapeutics to a functional concentration. While fluorescence output is a relative way to measure potency, retaining auxotrophic selection was an important design choice. High-efficiency yeast transformation enables the construction of libraries with 10 10 members, 40 a number greater than can be practically screened by FACS. Magneticactivated cell sorting is a solution for this in antibody display projects; 41 we argue, however, that library diversity can be significantly lowered using a far simpler life-or-death prescreening strategy that does not require additional laborious materials or protocols. By limiting auxotrophic selection to a prescreening step, the potential growth of false-positive or escape mutants that could overgrow the culture of desired drug variants is restricted.
A major advantage our system offers is the ability to normalize the output signal to cellular activity or health. While a drug may have a negative effect on cell health, it may still bind the intended target. In such a scenario, the GFP output may be low, yet this would be considered a positive signal if it was high relative to miRFP670. Such a hit could offer a functional starting point from which medicinal chemistry could optimize specificity.
An obstacle with any screening strategy is overcoming offtarget effects that produce a false-positive signal. Particularity for hard to hit targets, where the interface of protein dimers may not contain many druggable pockets, the likelihood of selecting an off-target hit might indeed be more probable. For example, this could be a hit binding the repressor and rendering it nonfunctional, or binding the activator and increasing its activity. To mitigate this risk, the control biosensor strain would act as a counter-screen to use against short-listed hits from the primary screen. Having a counterscreen in a separate strain also controls for biosensor escape mutants. Here, a nonsense mutation in the repressor or a mutation in one of the targets making them weakly interact will be registered as a positive signal in the primary screen, but as a negative signal in the secondary screen. Interestingly, the biosensor with bait and prey interaction showed greater reporter gene repression than the control biosensor. This difference could be explained by steric hindrance where the HIF-1 dimer might shield the activation domain from recruiting coactivators.
In conclusion, we have engineered a variation of the RTA biosensor system to enable both fluorescence and auxotrophic selections. The biosensor is constructed in a modular fashion, meaning it should be applicable to other PPI targets of interest. Additionally, we provide an option to control for off-target effects and the ability to screen for false-positives. We anticipate this biosensor will be useful as part of a drug discovery platform, particularly when screening DNA-encoded new modality drugs.

■ MATERIALS AND METHODS
Bacterial Strains and Growth. All plasmids were cloned and amplified in competent DH5α Escherichia coli cells. E. coli was cultured in lysogeny broth (LB) (10 g/L tryptone, 5 g/L yeast extract, 5 g/L NaCl, 5 mL/L 1 M Tris−HCl, and 20 g/L agar for solid media) and supplemented with appropriate antibiotics (ampicillin 100 mg/mL or chloramphenicol 34 mg/ mL). E. coli was cultured at 37°C, with shaking at 200 rpm for liquid cultures.
Yeast Strains and Growth. Saccharomyces cerevisiae CEN.PK 102-5B (MATa ura3-52 his3Δ1 leu2-3/112 MAL2-8 c SUC2) 42 was used as background for biosensor strain construction. S. cerevisiae was routinely cultured on yeast peptone dextrose (YPD) medium (10 g/L yeast extract, 20 g/ L peptone from casein, 20 g/L glucose, and 20 g/L agar for solid media) supplemented with appropriate antibiotics (Nourseothricin, 100 mg/L and Geneticin, 200 mg/L). Synthetic complete dextrose (SD) media (6.7 g/L yeast nitrogen base without amino acids, 20 g/L glucose, 0.79 g of complete supplement mixture, and 20 g/L agar for solid media) was used to maintain plasmids in yeast. Depending on selection, uracil or histidine were omitted. Yeast was cultured at 30°C and with shaking at 220 rpm for liquid cultures. Yeast was routinely transformed by chemical transformation following the lithium acetate protocol. 43 Growth Profiler. Time course measurement of yeast growth was monitored using a growth profiler (EnzyScreen). Yeast strains were inoculated from 24 h precultures to an OD 600 0.1 in 250 μL of media. Cultures were grown at 30°C with shaking at 250 rpm in a 96-well microtiter plate. A minimum of three biological replicates were used per measurement. Data analysis and figure creation were done with GraphPad Prism software.
Plasmid and Strain Construction and Verification. Plasmids were constructed using either Gibson assembly (Gibson Assembly Master Mix, New England Biolabs), Golden Gate assembly, or restriction ligation as per published protocols. 25,44 Routine DNA sequence verification was done by Eurofins Genomics. Plasmid and gel extraction kits from ThermoFisher Scientific were used to purify DNA for cloning. Polymerase chain reactions (PCR) were performed using Phusion DNA polymerase (ThermoFisher Scientific) for cloning. Yeast codon optimized genes were synthesized at either GenScript, Integrated DNA Technologies, or TWIST bioscience. Details of plasmids constructed in this study can be found in the supplementary sequences and strains file in the Supporting Information.
The EasyClone-MarkerFree strategy was used to genomically integrate expression cassettes into yeast. 44 Integrative cassettes were either prepared as per the EasyClone-Marker-Free strategy or amplified by PCR from plasmids with oligonucleotides found in Table S1 that provide homology to genomic sites. Genomic DNA was extracted from yeast by boiling colonies in 20 nM NaOH for 5 min, and 0.5 μL was used as a template to confirm genomic integrations by colony PCR as per the EasyClone-MarkerFree strategy, 44 or as template for plasmid parts. Colony PCR was performed using SapphireAmp as per the manufacturer's protocol. The genotype of strains constructed in this study can be found in the supplementary sequences and strains file in the Supporting Information.
Fluorescence Measurement. Real-time fluorescence was measured using a BioLector (m2p-laboratories GmbH, Baesweiler, Germany). Yeast strains were inoculated from 24 h old precultures to a starting OD 600 of 0.1 in 1 mL of media in 48-well FlowerPlates. GFP expression was measured as a ratio of GFP fluorescence to biomass. A minimum of three biological replicates were used per measurement. Data analysis and figure creation was done with GraphPad Prism software.
Flow cytometry was undertaken using a Guava easyCyte 8HT system (Merck Millipore). Yeast strains were inoculated from 24 h old precultures to a starting OD 600 of 0.1 in 250 μL ACS Synthetic Biology pubs.acs.org/synthbio Letter of media in a 96-well microtiter plate and grown for 16 h in a growth profiler (EnzyScreen). Yeast cultures were then diluted to an OD 600 of 0.02 in 200 μL of water and fluorescent proteins excited with a 488 nm laser (for GFP) or 648 nm laser (for miRFP670). 5000 events were recorded per sample. A minimum of three biological replicates were used per measurement. Fold over background fluorescence was calculated as the fold change in median fluorescence compared to the background yeast strain CEN.PK 102-5B. Data analysis and figure creation was done with GraphPad Prism and FlowJo software.
■ ASSOCIATED CONTENT

* sı Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.2c00274. biosensor output with dimerization weak or absent protein targets (Figures S1 and S2); growth curves for biosensor strains ( Figure S3); fluorescence output of biosensor strains ( Figure S4); oligonucleotides used in this study (Table S1) (PDF) Genotype and sequences of strains constructed (XLSX)